The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.AIC ANNs ARS BEA BP EKF FDI FIR FPE FTC GMDH IIR LMS MIMO MISO MLP
NLMS OBD OBE OBS PDF RTRN RUIF SSM UIF UKF ZBA Akaike Information
Criterion Artificial Neural Networks Adaptive Random Search Bounded-Erroranbsp;...